Novel Item Recommendation by User Profile Partitioning

Standard top-N collaborative recommendation algorithms are very poor at recommending relevant products to a user that are more novel than her average tastes. Our study shows that novel recommendation is difficult because standard similarity metrics measure the aggregate similarity to multiple items in the user profile and the influence of more novel items is lost in the aggregation. To better capture the user´s range of tastes, we propose to partition the user profile into clusters of similar items and compose the recommendation list of items that match well with each cluster, rather than with the entire user profile. In this paper we evaluate a number of partitioning strategies in combination with a dimension reduction strategy. A new evaluation methodology is introduced to capture the system ability to diversify its recommendations across relevant items regardless of their novelty. By plotting concentration curves of novelty against accuracy, we show that this strategy succeeds in reducing the system bias towards similar items at a small cost to overall accuracy.